Pre-processing of resting-state BOLD fMRI data. The problem of resting-state fMRI pre-processing is profound and urgently needs rigorous attention to ensure the continuing high quality of basic and clinical resting-state neuroimaging research in the future, particularly given the propensity for ever-more complex derivation of functional-connectivity based biomarkers. Most of the current pre-processing algorithms have originated from task-based fMRI data analysis and have subsequently been adopted for resting-state data analysis as well. However, a fundamental complication was overlooked at first: task-based fMRI data analysis is usually performed by correlating the fMRI data with an externally given task time series. Such an external measure of activation usually does not exist for functional-connectivity studies using resting-state data; instead, intrinsic “seed” measures such as a voxel signal, or the mean signal of a set of voxels, are considered. Since both seed signals and remaining brain signals are affected by the same artifactual processes during data acquisition, there is a danger of false-positive inflation of spurious functional-connectivity findings at rest, as has been shown in many recent studies that have focused on the effects of motion, respiration, etc.

Region-based spatial normalization. A major longstanding problem in functional neuroimaging studies of cognitive aging is that the large age-related changes in brain morphology make it difficult to co-register brains, a key step for studies comparing task-related activation in young and old groups. To address this issue, I propose to develop a region-based spatial normalization (RBSN) technique that will increase the accuracy of the fMRI data localization. RBSN aligns each neuroanatomical region of the human brain separately. In contrast, the prevailing spatial normalization method tries to align all regions of the brain at once. RBSN will therefore provide more accurate localization of activation and ensure that group analyses test the same brain area in each study participant. Better between-participant registration also provides additional statistical power to detect activation in regions that may not have reached the significance level using prevailing methods (i.e. reduce type II error). It may also rule out previously noted areas of activation that were detected for artifactual reasons (i.e., reduce type I error).

Effective measure of subjective task difficulty. Many studies suggest that observed old and young BOLD activation differences during memory task can be due to mismatches in the subjective task difficulty rather than actual differences in the neural networks processes underlying the task. In each participant, subjective task difficulty is an underlying driver for both behavioral performance and fMRI task-related brain activation (either as expression level of the same network or activation of a different network). Therefore, any study relating brain activation to behavioral performance is required to control for subjective task difficulty. Without this control, the increase in expression of fMRI activation pattern might be related to decrease in performance, increase in performance, or even varying results in performance. The subjective difficulty of a delayed-item-recognition task is often measured by either the accuracy or response-time. However, it is often impossible to measure difficulty using accuracy when the number of items is low due to ceiling effects. Conversely, response-time might be a better measure of subjective difficulty since it has a better association with task difficulty when the number of items is lower. However, when the number of items increases, response-time becomes less reliably associated with the task load.

Causal Markov random field. Conventional Markov random field is hampered by noncausality and its causal definitions are also not free of difficulties. For instance, the Markov mesh random field has strong diagonal dependency in its local neighboring system. I have introduced multilateral Markov random field (MMRF) to overcome this issue. MMRF is a MRF whereas the reverse may not hold. The joint distribution of a causal MRF is readily given by the product of the low-dimensional local distribution whereas in conventional MRF it is only given through Gibbs distribution. Low-dimensional joint pdf's are often estimated using a joint histogram for homogeneous field or by a few sample of the field for inhomogeneous fields. This makes the model closely tied to the image in use. So far MMRF is used for the computation of image entropy and mutual information.

Predictive models of AD progression.
The ability to predict the length of time from disease onset to need for institutionalization or to death in individual patients with Alzheimer’s disease (AD) has implications for patients, their caregivers, the design of intervention studies, and health policy and economics. Longitudinal Grade of Membership (L-GoM) model has many advantages over approaches previously used to model the progression of AD, including original Cox-based model. The L-GoM model allows large amounts of data on individual patients collected at every visit to be efficiently and accurately summarized using a small number of bounded continuous latent variables that represent the most salient characteristics of AD as it progresses over time. This multi-state model addresses the shortcoming of traditional AD staging, which typically views AD as progressing through discrete stages. In contrast, in this model the status of a patient at any time is captured using a combination of four latent variables, or pure types (PT).